# High risk, low rest: a new framework for monitoring sleep vulnerability in emergency medicine

## TL;DR

This paper introduces a new metric called the Shift Load Index to monitor sleep disruption in emergency healthcare workers, linking it to reduced sleep quantity and irregularity.

## Contribution

The study introduces the Shift Load Index (SLI) as a novel metric to quantify recovery constraints in shift work.

## Key findings

- Higher SLI scores are strongly associated with reduced time in bed and shorter sleep duration.
- SLI is significantly linked to lower Sleep Regularity Index scores, indicating irregular sleep patterns.
- SLI and sleep regularity together explain a notable portion of sleep disruption variance among emergency workers.

## Abstract

Shift work in emergency care settings disrupts circadian rhythms and sleep, increasing health risks and performance. A key aspect of addressing these challenges lies in predicting the burden of shift work to develop safer schedules. This study introduces the Shift Load Index (SLI) as an advanced and sensitive metric for quantifying recovery constraints and examined its association with objective sleep outcomes in emergency healthcare professionals.

A two-phase observational field study was conducted with 72 nurses and physicians from two French emergency departments. In the theoretical validation phase, 140 work shifts were analyzed using the SLI and compared to validated FAID Quantum fatigue scores. In the behavioral validation phase, weekly actigraphy data from 35 participants were analysed to assess time in bed, total sleep time, and Sleep Regularity Index (SRI). We employed generalized linear mixed-effects models to assess the association of SLI with sleep outcomes.

SLI scores significantly predicted FAID Quantum scores (all p < 0.001). Emergency healthcare professionals obtained on average 6h 09 min of sleep for 8h09min in bed, with irregular sleep patterns (mean SRI = 52%). Higher SLI scores were associated with reduced time in bed (β = −33.19, p < 0.001), shorter sleep (β = −18.30, p < 0.001), and lower SRI (β = −1.06, p < 0.001). SRI and total sleep time, as independent factors, together explained 48% of SLI variance (including random effects, 18% by fixed effects only).

Higher shift load is associated with both reduced sleep quantity and regularity. The SLI provides a useful tool to assess recovery burden, with potential applications in optimizing shift schedules and informing fatigue risk management strategies for emergency healthcare professionals.